TY - JOUR
T1 - Pilot Study to Establish a Novel Five-Gene Biomarker Panel for Predicting Lymph Node Metastasis in Patients With Early Stage Endometrial Cancer
AU - Huang, Chia Yen
AU - Liao, Kuang Wen
AU - Chou, Chih Hung
AU - Shrestha, Sirjana
AU - Yang, Chi Dung
AU - Chiew, Men Yee
AU - Huang, Hsin Tzu
AU - Hong, Hsiao Chin
AU - Huang, Shih Hung
AU - Chang, Tzu Hao
AU - Huang, Hsien Da
N1 - Publisher Copyright:
© Copyright © 2020 Huang, Liao, Chou, Shrestha, Yang, Chiew, Huang, Hong, Huang, Chang and Huang.
PY - 2020/1/21
Y1 - 2020/1/21
N2 - Introduction: In the United States and Europe, endometrial endometrioid carcinoma (EEC) is the most prevalent gynecologic malignancy. Lymph node metastasis (LNM) is the key determinant of the prognosis and treatment of EEC. A biomarker that predicts LNM in patients with EEC would be beneficial, enabling individualized treatment. Current preoperative assessment of LNM in EEC is not sufficiently accurate to predict LNM and prevent overtreatment. This pilot study established a biomarker signature for the prediction of LNM in early stage EEC. Methods: We performed RNA sequencing in 24 clinically early stage (T1) EEC tumors (lymph nodes positive and negative in 6 and 18, respectively) from Cathay General Hospital and analyzed the RNA sequencing data of 289 patients with EEC from The Cancer Genome Atlas (lymph node positive and negative in 33 and 256, respectively). We analyzed clinical data including tumor grade, depth of tumor invasion, and age to construct a sequencing-based prediction model using machine learning. For validation, we used another independent cohort of early stage EEC samples (n = 72) and performed quantitative real-time polymerase chain reaction (qRT-PCR). Finally, a PCR-based prediction model and risk score formula were established. Results: Eight genes (ASRGL1, ESR1, EYA2, MSX1, RHEX, SCGB2A1, SOX17, and STX18) plus one clinical parameter (depth of myometrial invasion) were identified for use in a sequencing-based prediction model. After qRT-PCR validation, five genes (ASRGL1, RHEX, SCGB2A1, SOX17, and STX18) were identified as predictive biomarkers. Receiver operating characteristic curve analysis revealed that these five genes can predict LNM. Combined use of these five genes resulted in higher diagnostic accuracy than use of any single gene, with an area under the curve of 0.898, sensitivity of 88.9%, and specificity of 84.1%. The accuracy, negative, and positive predictive values were 84.7, 98.1, and 44.4%, respectively. Conclusion: We developed a five-gene biomarker panel associated with LNM in early stage EEC. These five genes may represent novel targets for further mechanistic study. Our results, after corroboration by a prospective study, may have useful clinical implications and prevent unnecessary elective lymph node dissection while not adversely affecting the outcome of treatment for early stage EEC.
AB - Introduction: In the United States and Europe, endometrial endometrioid carcinoma (EEC) is the most prevalent gynecologic malignancy. Lymph node metastasis (LNM) is the key determinant of the prognosis and treatment of EEC. A biomarker that predicts LNM in patients with EEC would be beneficial, enabling individualized treatment. Current preoperative assessment of LNM in EEC is not sufficiently accurate to predict LNM and prevent overtreatment. This pilot study established a biomarker signature for the prediction of LNM in early stage EEC. Methods: We performed RNA sequencing in 24 clinically early stage (T1) EEC tumors (lymph nodes positive and negative in 6 and 18, respectively) from Cathay General Hospital and analyzed the RNA sequencing data of 289 patients with EEC from The Cancer Genome Atlas (lymph node positive and negative in 33 and 256, respectively). We analyzed clinical data including tumor grade, depth of tumor invasion, and age to construct a sequencing-based prediction model using machine learning. For validation, we used another independent cohort of early stage EEC samples (n = 72) and performed quantitative real-time polymerase chain reaction (qRT-PCR). Finally, a PCR-based prediction model and risk score formula were established. Results: Eight genes (ASRGL1, ESR1, EYA2, MSX1, RHEX, SCGB2A1, SOX17, and STX18) plus one clinical parameter (depth of myometrial invasion) were identified for use in a sequencing-based prediction model. After qRT-PCR validation, five genes (ASRGL1, RHEX, SCGB2A1, SOX17, and STX18) were identified as predictive biomarkers. Receiver operating characteristic curve analysis revealed that these five genes can predict LNM. Combined use of these five genes resulted in higher diagnostic accuracy than use of any single gene, with an area under the curve of 0.898, sensitivity of 88.9%, and specificity of 84.1%. The accuracy, negative, and positive predictive values were 84.7, 98.1, and 44.4%, respectively. Conclusion: We developed a five-gene biomarker panel associated with LNM in early stage EEC. These five genes may represent novel targets for further mechanistic study. Our results, after corroboration by a prospective study, may have useful clinical implications and prevent unnecessary elective lymph node dissection while not adversely affecting the outcome of treatment for early stage EEC.
KW - endometrial cancer
KW - lymph node metastasis
KW - prediction model
KW - RNA sequencing
KW - TCGA
UR - http://www.scopus.com/inward/record.url?scp=85079042442&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=85079042442&partnerID=8YFLogxK
U2 - 10.3389/fonc.2019.01508
DO - 10.3389/fonc.2019.01508
M3 - Article
AN - SCOPUS:85079042442
SN - 2234-943X
VL - 9
JO - Frontiers in Oncology
JF - Frontiers in Oncology
M1 - 1508
ER -